2 citations · 2 across the 3 of their papers we have counts for
4 papers
Learn Only Where You Can't Compute: Structured Corrections to Gaussian Process Posteriors
Tobias Weber, Carla Sagebiel, Philipp Hennig
Physics-informed Gaussian processes (GPs) provide a principled framework for scientific inference by expressing uncertainty over empirical measurements, boundary conditions, and go…
In-context learning to predict critical transitions in dynamical systems
Yunus Sevinchan, Juan Nathaniel, Kai Ueltzhöffer +8
Critical transitions - abrupt, often irreversible changes in system dynamics - arise across human and natural systems, often with catastrophic consequences. Real-world observations…
laplax -- Laplace Approximations with JAX
Tobias Weber, Bálint Mucsányi, Lenard Rommel +4
The Laplace approximation provides a scalable and efficient means of quantifying weight-space uncertainty in deep neural networks, enabling the application of Bayesian tools such a…
Linearization Turns Neural Operators into Function-Valued Gaussian Processes
Emilia Magnani, Marvin Pförtner, Tobias Weber +1
Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differenti…